The maximum capture problem with random utilities: Problem formulation and algorithms

Benati, Stefano; Hansen, Pierre

doi:10.1016/s0377-2217(01)00340-x

Cited by 105 publications

(58 citation statements)

References 18 publications

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“…The gravity approach can be classified as a special case of the random utility approach as was done by Benati and Hansen [9] on the network. The utility is ln…”

Section: The Gravity-based Approach To Estimating Market Sharementioning

confidence: 99%

A review of competitive facility location in the plane

Drezner

2014

Logist. Res.

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In this paper, we review competitive location models. Retail facilities operate in a competitive environment with an objective of profit and market share maximization. These facilities are different from each other in their overall attractiveness to consumers. The basic problem is the optimal location of one or more new facilities in a market where competition already exists or will exist in the future. Extensions to the models include an analysis of the optimal allocation of a budget among new facilities and their best locations, modeling location under conditions of uncertainty and future competition, incorporating the concept of a threshold in competitive location, modeling lost demand, and minimizing cannibalization.

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“…The gravity approach can be classified as a special case of the random utility approach as was done by Benati and Hansen [9] on the network. The utility is ln…”

Section: The Gravity-based Approach To Estimating Market Sharementioning

confidence: 99%

A review of competitive facility location in the plane

Drezner

2014

Logist. Res.

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“…The RUM is not only a popular form of economic model for consumer choice behavior, it also has its way in other related disciplines (Marley, 2002) such as marketing (e.g. Baltas et al 2001, Benati andHansen, 2002;Suárez et al, 2004) and transportation planning (e.g. Cascetta et al, 2002;Cascetta and Papola, 2001 (Veldhuisen, Timmermans, and Kapoen 2000), pedestrian movement, and dynamics of land use development (Arentze and Timmermans 2004).…”

Section: Shop-choice Modelmentioning

confidence: 99%

Simulating spatial market share patterns for impacts analysis of large-scale shopping centers on downtown revitalization

et al. 2011

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The decline of downtown has been observed in many cities across the world. In response, many small cities in Japan, for example, have been taking regeneration efforts including development controls upon large-scale shopping centres (B-shops). It is extremely useful to analyze potential effects of relevant planning policies before implementation. We developed an urban planning support tool, a multi-agent simulation (MAS) model called Shopsim-MAS, to investigate the impacts of some downtown revitalization policies through consequent spatial dynamics of shops' market shares. We discuss methods to model household behaviour and to understand the market area dynamics of shops. The Shopsim-MAS developed in this project proves to be a useful means to analyze the impact of downtown revitalization policies in Japan. It is also expected to be further expanded for impact analysis of similar or more sophisticated urban policies in other parts of the world.

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“…For example, de Palma et al (1989), Benati (1999) and Marianov et al (2008) discuss non-linear model formulations for discrete locational decisions. To the best of our knowledge, Benati and Hansen (2002) are the first who proposed a linear reformulation of the non-linear MNL. Their approach results in a hyperbolic sum integer problem.…”

Section: Introductionmentioning

confidence: 99%

“…Aros-Vera et al (2013) apply this approach to the planning of park-and-ride facilities. Finally, Zhang et al (2012) propose an alternative approach similar to Benati and Hansen (2002). Haase and Müller (2014a) show that a variant of the model of Haase (2009) seems to be superior to the formulations of Benati and Hansen (2002) and Zhang et al (2012).…”

Section: Introductionmentioning

confidence: 99%

“…We denote F as the objective function value of (2). In literature, we find exact linear reformulations of (1) such that (2)-(4) can be modeled as a mixedinteger program: Haase (2009) and Aros-Vera et al (2013) employ specific properties of the MNL, while Zhang et al (2012) propose an approach based on variable substitution similar to Benati and Hansen (2002). In Sect.…”

Section: Introductionmentioning

confidence: 99%

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Customer segmentation in retail facility location planning

Müller

Haase

2014

Bus Res

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In this contribution, we discuss a facility location model to maximize firms’ patronage, while demand is determined by a multinomial logit model (MNL). We account for customer segmentation based on customer characteristics. Hence, we are able to reduce the bias to the objective, which is due to constant substitution patterns of the MNL. Numerical studies show that averaging customer characteristics yield a bias of more than 15 % of the objective function value compared to segmentation. Using GAMS/CPLEX, we are able to solve problem sets with 2 segments, 500 demand points and 10 potential locations to optimality in 1 h computation time. If we consider 50 potential locations, the gap reported by CPLEX is $$<$$ < 8 % in 1 h. We present an illustrative case example of a furniture store company in Germany (data are available as electronic supplementary material to this article). The corresponding problem is solved to optimality in a few minutes.

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The maximum capture problem with random utilities: Problem formulation and algorithms

Cited by 105 publications

References 18 publications

A review of competitive facility location in the plane

A review of competitive facility location in the plane

Simulating spatial market share patterns for impacts analysis of large-scale shopping centers on downtown revitalization

Customer segmentation in retail facility location planning

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